Aggressive Stocks: Daily Forecast Evaluation Report

Executive Summary

In this stock market forecast evaluation report, we will examine the performance of the forecasts generated by the I Know First AI Algorithm for the Aggressive stocks for long and short positions which were sent daily to our customers. Our analysis covers the period from December 20, 2019, to September 9th, 2020. 

Chart 1: Performance comparison for Top 20, Top 10, and Top 5 signals for Aggressive stocks vs S&P 500 for shorter-term horizons since December 20th, 2020 until September 9th, 2020
Chart 2: Performance comparison for Top 20, Top 10, and Top 5 signals for Aggressive stocks vs S&P 500 for longer-term horizons since December 20th, 2020 until September 9th, 2020

Top Aggressive Stocks Highlights

  • The Top 20, Top 10, and Top 5 signal groups generated by I Know First consistently outperformed S&P 500 Index for the short and long term horizons.
  • All the groups returns had a good performance, especially the ones for the 3 Months time horizon. Every group registered a return higher than 10%.
  • The Top 5 signal group outperformed the S&P 500 for almost 9 times for the 14 days time horizon.

About the I Know First Algorithm

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The I Know First self-learning algorithm analyzes, models, and predicts the stock market. The algorithm is based on Artificial Intelligence (AI) and Machine Learning (ML) and incorporates elements of Artificial Neural Networks and Genetic Algorithms.

The system outputs the predicted trend as a number, positive or negative, along with a wave chart that predicts how the waves will overlap the trend. This helps the trader to decide which direction to trade, at what point to enter the trade, and when to exit. Since the model is 100% empirical, the results are based only on factual data, thereby avoiding any biases or emotions that may accompany human derived assumptions.

The human factor is only involved in building the mathematical framework and providing the initial set of inputs and outputs to the system. The algorithm produces a forecast with a signal and a predictability indicator. The signal is the number in the middle of the box. The predictability is the number at the bottom of the box. At the top, a specific asset is identified. This format is consistent across all predictions.

Our algorithm provides two independent indicators for each asset – Signal and Predictability.

The Signal is the predicted strength and direction of the movement of the asset. Measured from -inf to +inf.

The predictability indicates our confidence in that result. It is a Pearson correlation coefficient between past algorithmic performance and actual market movement. Measured from -1 to 1.

You can find a detailed description of our heatmap here.

Evaluating US Stock Forecasts: Aggressive Socks Package

We choose stocks from the aggressive stocks by taking the top 30 most predictable assets, and then we apply a set of signal-based filters: top 20, 10 and 5 based on signals. By doing so we focus on the most predictable assets on the one hand, while capturing the ones with the highest signal on the other.

For the analysis we group the forecasts by absolute signals since these strategies are long and short. If the signal is positive, then we buy and if negative, we short.

The Stock Market Forecast Performance Evaluation Method

We perform evaluations on the individual forecast level. It means that we calculate what would be the return of each forecast we have issued for each horizon in the testing period. Then, we take the average of those results by forecast horizon.

For example, to evaluate the performance of our 1-month forecasts, we calculate the return of each trade by using this formula:

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This simulates a client purchasing the asset based on our prediction and selling it exactly 1 month in the future.

We iterate this calculation for all trading days in the analyzed period and average the results.

Note that this evaluation does not take a set portfolio and follow it. This is a different evaluation method at the individual forecast level.

The Hit Ratio Method

The hit ratio helps us to identify the accuracy of our algorithm’s predictions.

Using our Daily Forecast asset filtering, we predict the direction of the movement of different assets. Our predictions are then compared against actual movements of these assets within the same time horizon.

The hit ratio is then calculated as follows:

S&P500

For instance, a 90% hit ratio for a predictability filter with a top 10 signal filter would imply that the algorithm correctly predicted the price movements of 9 out of 10 assets within this particular set of assets.

The Benchmarking Method: S&P 500

In order to evaluate our algorithm’s performance, we used the S&P 500 index as a benchmark.

The S&P 500 measures the stock performance of 500 of the U.S’ largest publicly traded companies. It is one of the most followed equity indices and is frequently used as the best gauge of large cap US equities. The S&P is often used as a benchmark for the performance of US publicly traded companies, and the US market as a whole. The S&P 500 is a capitalization-weighted index, the weight of each company in the index is determined based on its market cap divided by the aggregate market cap of all the S&P 500 companies.

For each time horizon, we compare the S&P 500 performance with the performance of our forecasts.

Performance Evaluation: Overview

In this report, we conduct testing for the Aggressive stocks that I Know First covers by its algorithmic forecast. The period for evaluation and testing is from December 20th, 2019 to September 9th, 2020. During this period, we were providing our clients with daily forecasts for Aggressive stocks in short term and long term time horizons spanning from 3 days to 3 months which we evaluate in this report.

Aggressive Stocks Evaluation

Table 1: Daily forecasts average performance for time horizons from 3 days to 3 Months vs S&P 500 for time horizons 3 days to 3 months since December 20th, 2019 until September 9th, 2020

Signals for longer time horizons performed extremely well between December 20th and September 9th. Every signal group for every time horizon outperformed the S&P 500. For the longest time horizons, the top 10 signal group exponentially outperformed the benchmark index. That group had a return of 16.58% for 3 Months forecasts, in comparison to the S&P 500’s return of 3.02% and a 3.68% return for 1 Month forecasts, substantially greater than the S&P 500 gain of 1.17%.

Table 2: Daily forecasts hit ratio for time horizons from 3 days to 3 Months since December 20th, 2019 until September 9th, 2020.

In addition to considerably outperforming the S&P 500, our shorter time frames signals all had a hit ratio of around 50% for 7 and 14 days time horizons. Even though those results seem low the average returns are positive and beating the benchmark. This is significant because, in addition to having tremendous returns on a long horizon, we also have demonstrated consistency for positive gains in short time frames.

Conclusion

This evaluation report presented the performance of I Know First’s algorithm for Opportunities on Aggressive stocks forecasts from December 20th, 2019 to September 9th, 2020. It shows the average returns and hit ratios for all time horizons.

The I Know First algorithm has obtained better performance for both short term and long term time horizons. It succeeded in generating significant positive returns for every signal group and time horizon. It is important to note that almost every signal group across short-horizon gave a hit ratio of around 50%, showing consistent and reliable accuracy.  It is advisable for traders to rebalance their portfolio using the most updated forecasts sent by I Know First.

We look forward to new market data in the following months and will monitor the changes in performance trends that are going to be communicated to our investors and subscribers in the follow-up reports.